The present invention relates to medical imaging of the heart, and more particularly, to automatic detection of coronary stenosis in cardiac computed tomography (CT) image data.
Coronary stenosis is the constriction or narrowing of a coronary artery, and is among the leading causes of heart attack. Coronary stenosis is typically caused by fat, cholesterol, and other substances that clog the coronary arteries over time. Traditionally coronary stenosis was detected using angiography, which is an invasive procedure. However, medical imaging of the heart, such as computed tomography (CT) imaging can be used to non-invasively detect coronary stenosis. Coronary stenosis is typically classified into two types: calcified plaque and non-calcified plaque. The calcified plaque usually appears as bright regions in contrast enhanced CT images, while the non-calcified plaque appears as darker regions in the arteries.
Manual detection and segmentation of stenosis in cardiac CT images is not only a tedious task, but is also subject to inter-observer variability. Accordingly, automatic stenosis detection in CT images is desirable. In Igsum et al., “Detection of Coronary Calcifications from Computed Tomography Scans for Automated Risk Assessment of Coronary Artery Disease”, Medical Physics, April 2007, a heart and aorta segmentation is applied to native CT data sets and specific features are used automatically detect coronary calcifications using a two-stage calcification detection system with a k-NN (Nearest Neighbor) classifier and a feature selection scheme. This technique was able to detect 73.8% of the calcified plaques with an average of 0.1 false positives per scan. Although fully automatic, this method was specifically designed for calcium scoring, and does not detect non-calcified plaques. More recently, Stefan et al., “Automatic Detection of Calcified Coronary Plaques in Computed Tomography Data Sets”, MICCAI, 2008, 170-177, proposed a framework for the automatic detection of calcified plaques. This framework made use of both angio (i.e., contrast weighted) and native (i.e., non-contrast weighted) CT data sets. This framework was able to achieve an 85.5% detection rate for calcified plaques with a positive prediction value (PPV) of 87.8%.